Load deviations between the output of ultra-supercritical(USC)coal-fired power units and automatic generation control(AGC)commands can adversely affect the safe and stable operation of these units and grid load dispat...Load deviations between the output of ultra-supercritical(USC)coal-fired power units and automatic generation control(AGC)commands can adversely affect the safe and stable operation of these units and grid load dispatching.Data-driven diagnostic methods often fail to account for the imbalanced distribution of data samples,leading to reduced classification performance in diagnosing load deviations in USC units.To address the class imbalance issue in USC load deviation datasets,this study proposes a diagnostic method based on the multi-label natural neighbor boundary oversampling technique(MLNaNBDOS).The method is articulated in three phases.Initially,the traditional binary oversampling strategy is improved by constructing a binary multi-label relationship for the load deviations in coal-fired units.Subsequently,an adaptive adjustment of the oversampling factor is implemented to determine the oversampling weight for each sample class.Finally,the generation of new instances is refined by dynamically evaluating the similarity between new cases and natural neighbors through a random factor,ensuring precise control over the instance generation process.In comparisons with nine benchmark methods across three imbalanced USC load deviation datasets,the proposed method demonstrates superior performance on several key evaluation metrics,including Micro-F1,Micro-G-mean,and Hamming Loss,with average values of 0.8497,0.9150,and 0.1503,respectively.These results substantiate the effectiveness of the proposed method in accurately diagnosing the sources of load deviations in USC units.展开更多
With the rapid growing of EVs and increasing power loads,the integrated energy systems(IES)in practical operations are facing challenges in balancing safety and economic efficiency,along with the rise of unexpected en...With the rapid growing of EVs and increasing power loads,the integrated energy systems(IES)in practical operations are facing challenges in balancing safety and economic efficiency,along with the rise of unexpected energy usage plans by users.To address these issues,this research proposes a three-layer game-based multi-objective optimization strategy for IES.First,safety performance indexes of the in-tegrated energy network are established using graph theory and the Wiener process.Then,a non-cooperative-Stackelberg-cooperative game framework is constructed,which optimizes safety and eco-nomic indexes while allowing lower-level users to cooperate to maximize their own benefits.Further-more,considering Unexpected Load Deviations(ULDs)during actual operations,a flexible resource margin adjustment-based Adaptive Optimal Strategy and Information Gap Decision Theory(AOS-IGDT)strategy is proposed and embedded in the second stage of rolling optimization.Finally,the proposed strategy is verified using the coupled IEEE 33-bus system and a 17-node thermal network,the results demonstrate its effectiveness in achieving a win-win outcome for system economic and safety perfor-mance while reducing the ULDs and improving the benefits of all stakeholders.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62173050)Shenzhen Municipal Science and Technology Innovation Committee(Grant No.KCXFZ20211020165004006)+3 种基金Natural Science Foundation of Hunan Province of China(Grant No.2023JJ30051)Hunan Provincial Graduate Student Research Innovation Project(Grant No.QL20230214)Major Scientific and Technological Innovation Platform Project of Hunan Province(2024JC1003)Hunan Provincial University Students’Energy Conservation and Emission Reduction Innovation and Entrepreneurship Education Center(Grant No.2019-10).
文摘Load deviations between the output of ultra-supercritical(USC)coal-fired power units and automatic generation control(AGC)commands can adversely affect the safe and stable operation of these units and grid load dispatching.Data-driven diagnostic methods often fail to account for the imbalanced distribution of data samples,leading to reduced classification performance in diagnosing load deviations in USC units.To address the class imbalance issue in USC load deviation datasets,this study proposes a diagnostic method based on the multi-label natural neighbor boundary oversampling technique(MLNaNBDOS).The method is articulated in three phases.Initially,the traditional binary oversampling strategy is improved by constructing a binary multi-label relationship for the load deviations in coal-fired units.Subsequently,an adaptive adjustment of the oversampling factor is implemented to determine the oversampling weight for each sample class.Finally,the generation of new instances is refined by dynamically evaluating the similarity between new cases and natural neighbors through a random factor,ensuring precise control over the instance generation process.In comparisons with nine benchmark methods across three imbalanced USC load deviation datasets,the proposed method demonstrates superior performance on several key evaluation metrics,including Micro-F1,Micro-G-mean,and Hamming Loss,with average values of 0.8497,0.9150,and 0.1503,respectively.These results substantiate the effectiveness of the proposed method in accurately diagnosing the sources of load deviations in USC units.
基金supported by the Key Laboratory of Smart Grid in Shaanxi Province.
文摘With the rapid growing of EVs and increasing power loads,the integrated energy systems(IES)in practical operations are facing challenges in balancing safety and economic efficiency,along with the rise of unexpected energy usage plans by users.To address these issues,this research proposes a three-layer game-based multi-objective optimization strategy for IES.First,safety performance indexes of the in-tegrated energy network are established using graph theory and the Wiener process.Then,a non-cooperative-Stackelberg-cooperative game framework is constructed,which optimizes safety and eco-nomic indexes while allowing lower-level users to cooperate to maximize their own benefits.Further-more,considering Unexpected Load Deviations(ULDs)during actual operations,a flexible resource margin adjustment-based Adaptive Optimal Strategy and Information Gap Decision Theory(AOS-IGDT)strategy is proposed and embedded in the second stage of rolling optimization.Finally,the proposed strategy is verified using the coupled IEEE 33-bus system and a 17-node thermal network,the results demonstrate its effectiveness in achieving a win-win outcome for system economic and safety perfor-mance while reducing the ULDs and improving the benefits of all stakeholders.
